JP6535173B2 - Method and apparatus for optimizing configuration of distributed energy system - Google Patents

Description

  The present invention relates to a method and apparatus for configuration optimization of distributed energy systems.

  In recent years, large-scale facilities such as industrial estates, factories and business buildings are provided with many facilities that consume energy such as power and heat. A distributed energy system combining a plurality of energy supply devices has been proposed as a source of energy to be supplied. In the equipment planning of such a distributed energy system, it is required to optimize the equipment configuration.

  Generally, since the evaluation function of the configuration optimization includes the operation cost, it is necessary to optimize not only the configuration but also the operation in the configuration optimization. If the calculation model of operation optimization is detailed, the accuracy of the solution increases but the number of decision variables increases, and the calculation takes a very long time.

  On the other hand, conventionally, a method of collectively optimizing the configuration and operation using mixed integer linear programming by simplifying the calculation model of operation optimization and reducing the determination variables (see, for example, Patent Document 1), There is a method (see, for example, Patent Document 2) of separating configuration determination and operation optimization calculation using a genetic algorithm.

JP, 2009-26092, A JP-A-11-39004

  However, the method of Patent Document 1 can not consider practical operation and can not obtain a practical solution. The method of Patent Document 2 has a problem that the optimal search of the solution is not guaranteed because it is a random search method. is there.

  Therefore, the present invention has been made to solve the problems as described above, and it is an object of the present invention to optimize the configuration of devices of a distributed energy system in a practical and highly accurate manner.

<Configuration optimization method>
In order to solve the above problems, the configuration optimization method of a distributed energy system according to an aspect of the present invention optimizes the configuration of equipment in facility planning of a distributed energy system in which energy is supplied by a plurality of energy supply devices. Method to minimize the arbitrary evaluation function including the operation cost under any constraint conditions. For the configuration optimization problem, the maximum number of selectable constituent devices is one. A few configuration optimization problems are created as a plurality of subproblems, and the solution of the original problem is found by solving the subproblems recursively.

  Here, the solution to a configuration optimization problem is either the maximum number of configurations (configuration using the maximum number of components selectable in the problem) or the solution of subproblems. Furthermore, the maximum number of configurations may be solved only if there is no solution that satisfies the constraint condition for a partial problem, or if there is a solution for the partial problem that has the maximum number configuration of problems. Consider and reduce the computational load by calculating the evaluation function only when the maximum number configuration is likely to be a solution. This is because, generally, increasing the number of energy devices increases equipment costs and maintenance costs, and it is basically more efficient to operate one at rated load than to operate two at partial load in operation as well. When the number of certain devices increases by one and the evaluation function becomes worse, it is based on the inventors' knowledge that the evaluation function does not improve even if the number of the same devices is further increased.

  According to the above configuration, since the determination of the configuration of the device and the calculation of the operation optimization can be separated, the calculation model of the operation optimization is simplified as compared with the conventional method of collectively optimizing the configuration and the operation. It is not necessary to obtain an accurate solution. In addition, since it is a recursive method, partial problems will be solved bottom-up, and only the minimum configuration that can be a solution among them is used for operation optimization calculation, so the calculation load is small and practical The optimal configuration can be determined within typical computing time. Moreover, it is not a random search method but a full search method, and it is considered that a global optimum solution can be obtained except in a specific case.

  In addition, when there is no solution that satisfies the constraint condition in the configuration optimization problem or its subproblem, a configuration in which the evaluation function value is the smallest after adding a penalty to the evaluation function value according to the degree of not satisfying the constraint condition Solution. According to the above configuration, it can be considered that the maximum number configuration of the configuration optimization problem may be solved only when the solution of the partial problem is the maximum number configuration even if the constraints are not satisfied. Therefore, the computational load can be further reduced.

<Configuration Optimization Device>
The configuration optimization device for a distributed energy system according to another aspect of the present invention is a device for optimizing the configuration of a device in facility planning of a distributed energy system in which energy is supplied by a plurality of energy supply devices, For a configuration optimization problem that seeks the configuration of a device that minimizes any evaluation function including cost under any constraint conditions, multiple configuration optimization problems with one less maximum number of selectable components can be selected. The computation unit is provided as a subproblem and generating a solution of the original problem by recursively solving the subproblem.

  If the computing unit does not have a solution satisfying the constraint condition for at least one subproblem among the plurality of subproblems, or a solution of at least one subproblem among the plurality of subproblems is the maximum In the case of several configurations, the solution of the configuration optimization problem satisfies the constraints among the maximum number configurations of the configuration optimization problem or the solutions of the plurality of subproblems, and the evaluation function The solution of any subproblem of the plurality of subproblems satisfies the constraint, and the solution of any subproblem of the subproblem is not the maximum number composition of the subproblems. In the case, the solution of the configuration optimization problem may be configured such that the value of the evaluation function is the smallest among the solutions of the plurality of subproblems.

  The calculation unit adds a penalty to the value of the evaluation function according to the degree of failure to satisfy the restriction condition when there is no solution satisfying the restriction condition in the partial problem, and the value of the evaluation function to which the penalty is added is If the solution of at least one of the plurality of partial problems is the maximum number configuration of the partial problems, the solution of the configuration optimization problem is the solution. Among the maximum number configuration of the optimization problem or the solution of the plurality of subproblems, the value of the evaluation function to which the penalty is added is the smallest, and the solution of any subproblem of the plurality of subproblems The solution of the configuration optimization problem is configured such that the value of the evaluation function to which the penalty is added is the smallest among the solutions of the plurality of subproblems, if the problem is not in the maximum number configuration. Composed It may be.

  According to the present invention, the device configuration of the distributed energy system can be optimized practically and with high accuracy.

It is a block diagram which shows an example of a distributed energy system which is a process target of the structure optimization apparatus which concerns on one Embodiment of this invention, and a consumer's. It is a block diagram and a data flow figure showing an example of composition of a configuration optimization device of a distributed energy system of FIG. It is a functional block and data flow figure which perform the structure optimization method in the calculating part of FIG. It is a figure which shows an example of the partial problem expand | deployed by the recursive process of the structure optimization part of FIG. It is a flowchart which shows an example of the structure optimization method by the structure optimization apparatus of FIG. It is a flowchart which shows the detail of the structure optimization calculation of FIG.

  Embodiments of the present invention will be described with reference to the drawings. In the following, the same or corresponding elements are denoted by the same reference numerals throughout all the drawings, and redundant description will be omitted.

  FIG. 1 is a block diagram showing an example of the configuration of a distributed energy system to be processed by a configuration optimization apparatus according to an embodiment of the present invention, and a customer. As shown in FIG. 1, the distributed energy system 20 is a supply source for supplying energy to the customer 30. The customer 30 is constituted by a facility 31 that consumes energy. The customers 30 include, for example, facilities such as industrial estates, factories and business buildings, and consumption places such as cities.

  The distributed energy system 20 is configured by a plurality of energy supply devices 21. Energy is secondary energy produced by conversion from a primary energy source, such as, for example, power and steam (heat).

  An energy supply device (hereinafter, also simply referred to as a device) 21 is a device that converts a primary energy source into secondary energy. Examples of the energy supply device 21 include types such as a coal thermal power generation apparatus (coal power plant), a gas turbine, a gas engine, a fuel cell, solar power generation, an absorption refrigerator, and a boiler. In the present embodiment, the energy supply device 21 is configured by m types (m is an integer of 1 or more) of selectable devices 1 to m.

  FIG. 2 is a block diagram and a data flow diagram showing an example of the configuration of an apparatus for optimizing the configuration of the distributed energy system 20 of FIG. 1 (hereinafter simply referred to as the configuration optimization apparatus). As shown in FIG. 2, parameters are input to the configuration optimization apparatus 10 by an input device (not shown). Here, the parameters include information on the configuration optimization problem 10A of the distributed energy system 20, the demand input condition 10B, and the database 10C of the device.

  The information related to the configuration optimization problem 10A is information on the types of selectable constituent devices of the distributed energy system 20, the minimum number of installation for each device, and the maximum number of installation. The information related to the demand input condition is information such as a demand pattern such as electric power and steam, a fuel unit price, a purchase unit price, and a number of evaluation years. The information related to the device database is information such as device characteristics, equipment costs, maintenance costs, and the like.

  The configuration optimization device 10 includes an arithmetic unit 11 and a storage unit 12, and is configured by, for example, a computer. Arithmetic unit 11 and storage unit 12 are each configured of, for example, a CPU of a computer and an internal memory.

The calculation unit 11 performs calculation processing for executing the configuration optimization method of the distributed energy system 20 based on the input parameters, and outputs the optimum configuration 10D. The here optimal configuration 10D, a selectable m types of equipment optimum installation number of each device (device 1 Equipment _{m) (n 1 ~n m)} .

  The storage unit 12 stores parameters, an evaluation function, a configuration optimization program and other information. The configuration optimization program is a program for causing a computer (calculation unit 11) to execute the configuration optimization method of the distributed energy system 20. The configuration optimization program is not limited to one stored in the storage unit 12 incorporated in the computer, but may be one stored in a storage medium or another storage unit, or may be input by an input device. Or may be received via a network.

  FIG. 3 is a functional block and data flow diagram for executing the configuration optimization method in the operation unit 11 of FIG. As shown in FIG. 3, the calculation unit 11 realizes each function block including the configuration optimization unit 15, the running evaluation unit 16, the initial evaluation unit 17, and the total evaluation unit 18 (operates as each function block) ) Is configured. Here, 15A to 15E indicate data used for operations between blocks.

In order to solve the input configuration optimization problem 10A, the configuration optimization unit 15 creates, as a plurality of partial problems 15A, a configuration optimization problem in which the maximum number of selectable components of the configuration optimization problem 10A is smaller by one. . The configuration optimization problem 10A is a problem of finding a configuration of an apparatus that minimizes any evaluation function including the operation cost under any constraint.
Expressing the configuration optimization problem P ₀ given here as (n ₁ ^max , n ₂ ^max ,..., N _m ^max ) using the maximum number n ₁ ^{max to} n _m ^max of selectable components: There are m subproblems 15A, and they become P _{1 to} P _m below.

(P ₁ ) (n ₁ ^max −1, n ₂ ^max ,..., N _m ^max )
(P ₂ ) (n ₁ ^max , n ₂ ^max −1,..., N _m ^max )
:
(P _m ) (n ₁ ^max , n ₂ ^max ,..., N _m ^max −1) (1)

  Furthermore, in order to solve the created partial problem 15A, the configuration optimization unit 15 is a recursive process that can be called by newly considering the partial problem 15A as the configuration optimization problem 10A.

FIG. 4 is a view showing an example of the partial problem 15A developed by the recursive process of the configuration optimization unit 15. Three types of selectable constituent devices (m = 1, 2, 3, for example, device m = 1 is a gas turbine) , Equipment m = 2 gas engine, equipment m = 3 boilers), minimum installation number of each equipment is n ₁ ^min = n ₂ ^min = n ₃ ^min = 0, maximum installation number is n ₁ ^max = n ₂ ^max = n ₃ is an exploded view of a portion problems for ^max = 2 configuration optimization problem (2,2,2). The upper element and the lower element in FIG. 4 are in the relation between the original configuration optimization problem and the partial problem, and the partial problems corresponding to the original configuration optimization problem are connected by a line. For example, in the figure, the configuration optimization problem (1, 2, 2) in which the maximum number of selectable constituent devices is one less than the configuration optimization problem (2, 2, 2) positioned at the top is 2, 1, 2) and (2, 2, 1) are created as a plurality of subproblems 15A. Then, the partial problems (1, 2, 2), (2, 1, 2), (2, 2, 1) are newly regarded as the configuration optimization problem 10A by the recursive process, and the selectable component devices Configuration optimization problems with one less maximum number (0, 2, 2), (1, 1, 2), (1, 2, 1), (2, 0, 2), (2, 1, 1) , (2, 2, 0) are created as a plurality of subproblems 15A.

  As can be seen from FIG. 4, the subproblem 15A is often common to a plurality of upper problems, but the solution of the subproblem 15A obtained once is recorded in the storage unit 12 and the calculation result is reused. Calculation time can be shortened. In the present embodiment, an example is shown in which the number of combinations of configurations is small so that a developed view can be illustrated, but the configuration optimization problem 10A may actually be tens of thousands to hundreds of thousands of combinations.

The configuration optimization unit 15 searches the configuration optimization problems 10A for configurations (numbers of installation n _{1 to} n _{m of} devices 1 to _m ) 15B that can be solutions other than the solution of the partial problem 15A. . Specifically, the maximum number of configurations of the configuration optimization problem 10A (a configuration using the maximum number of components selectable in the problem, that is, n ₁ = n ₁ ^max , n ₂ = n ₂ ^max , ..., Determine whether there is a possibility that n _m = n _m ^max ) is a solution. In the present embodiment, for example, it is considered that the maximum number configuration of the configuration optimization problem 10A may be a solution only when there is a solution of the subproblem 15A having the maximum number configuration of the problems. . This is based on the knowledge that, in general, in the case of an energy facility, if the number of certain devices is increased by one and the evaluation function becomes worse, the evaluation function will not be improved even if the number of same devices is further increased. .

  The configuration optimization unit 15 outputs, to the running evaluation unit 16 and the initial evaluation unit 17, the configuration 15B determined that the configuration optimization problem 10A may be a solution other than the solution of the subproblem 15A, and the configuration 15B. The total cost (evaluation function) 15E of is obtained from the total evaluation unit 18. Then, the smallest configuration of the evaluation function as compared with the solution of the partial problem 15A is used as the solution of the configuration optimization problem 10A, and is stored in the storage unit 12 together with the value of the evaluation function.

The configuration optimization unit 15 outputs the solution of the configuration optimization problem 10A in the first call of the recursive process as the optimal configuration (the optimum installation numbers n _{1 to} n _{m of the} device 1 to the device _m ) 10D. In the present embodiment, as described later, the configuration optimization unit 15 solves from the subproblem 15A where the maximum installation number is equal to the minimum installation number in a bottom-up manner, and finally the configuration in the first call of the recursive process Find a solution for the optimization problem 10A.

  The running evaluation unit 16 calculates the equation (2) based on the demand pattern, the fuel unit price, the unit price of electricity purchase, the device characteristics (efficiency, operation restriction), the maintenance cost, and the information on the configuration 15 B input from the configuration optimization unit 15. The running cost 15 C of is calculated and output to the total evaluation unit 18.

Here, D _k is the number of days of season k, and F _{k, t} is the minimum operation cost at time t of season k when devices 1 to m are respectively installed in n _{1 to nm} units. The operation costs include the unit price of fuel for each device, the unit price of electricity purchase, maintenance costs, etc. In addition, if it can not meet the energy demand for the season / time, it penalizes the operation cost according to the energy shortage. In addition, the energy demand may include a plurality of electric power, steam, hot water, cold water and the like at the same time. In order to obtain F _{k, t} , it is necessary to decide the start / stop status of the installed equipment (for n ₁ + n ₂ + ... + _nm units), the operation load, the amount of purchased electricity, etc. so as to minimize the operation cost. This can be determined using the existing operation optimization method (see, for example, Japanese Patent Application Laid-Open No. 2004-317049).

  The initial evaluation unit 17 calculates the initial cost 15D of equation (3) based on the equipment cost of the device database and the information on the configuration 15B input from the configuration optimization unit 15, and outputs this to the total evaluation unit 18. Do.

  Here, Cm is the equipment cost of the device m.

  The total evaluation unit 18 calculates the total cost of the equation (4) based on the running cost 15C input from the running evaluation unit 16, the initial cost 15D input from the initial evaluation unit 17, and information on the evaluation year Y (evaluation A function J) 15E is calculated, and this is output to the configuration optimization unit 15.

Next, the procedure of the configuration optimization calculation will be described with reference to FIGS. First, the configuration optimization apparatus 10 acquires parameters (step S1 in FIG. 5). It is information related to the configuration optimization problem 10A of the distributed energy system 20, the demand input condition 10B, and the database 10C of the device. In this embodiment, as parameters related to the configuration optimization problem 10A, types of selectable component devices m (m = 1, 2, 3), and the minimum number of installed devices of each device (n ₁ ^min = n ₂ ^min = n ₃ ^min = 0) The maximum installation number (n ₁ ^max = n ₂ ^max = n ₃ ^max = 2) is input.

Next, the configuration optimization device 10 obtains the optimal configuration of the energy device (the optimum number of installation n _{1 to} n _{m of the} device 1 to the device _m ) (step S2 in FIG. 5). In step S2, first, as shown in FIG. 6, the configuration optimization unit 15 determines whether the minimum number of installation in the given configuration optimization problem is equal to the maximum number of installation (step S21). In the present embodiment, the minimum installation number of each device is 0 and the maximum installation number is 2 for the configuration optimization problem 10A given first, so the minimum installation number and the maximum installation number are different. If the minimum number of installations and the maximum number of installations are different (NO in step S21), configuration optimization unit 15 optimizes the configuration for which the maximum number of selectable components is less for one given configuration optimization problem 10A. The problem is created as a plurality of partial problems 15A (step S24). For example, for a given configuration optimization problem (2, 2, 2), the configuration optimization problem (1, 2, 2), (2, 1, 2) in which the maximum number of selectable components is one less , (2, 2, 1) as a subproblem 15A (see the top of FIG. 4).

Next, the configuration optimization unit 15 refers to the storage unit 12 to check whether or not the calculation for the subproblem 15A is completed (step S25), and if not already calculated, the subproblem 15A is newly added. Step S2 is called recursively on the assumption that the configuration optimization problem 10A, and the configuration optimization operation is executed (step S26). Here, three processes for respectively solving configuration optimization problems (1, 2, 2), (2, 1, 2), and (2, 2, 1) are called. Furthermore, for example, in a process given a configuration optimization problem (1, 2, 2) among three processes, subproblems (0, 2, 2), (1, 1, 2), (1, 2) , 1) create and call three processes to solve this. Thus, the configuration optimization unit 15 creates a subproblem 15A and calls a process for solving it until the maximum number of installed configuration optimization problems 10A given to the process becomes equal to the minimum number of installations. . For example, in a process given a configuration optimization problem (0, 0, 1), a subproblem (0, 0, 0) with the maximum number of installations equal to the minimum number of installations is created, and the configuration optimization problem (0, 0) , 0) is called (see the bottom of FIG. 4). If the minimum installation number and the maximum installation number are equal (YES in step S21), the configuration optimization unit 15 solves the configuration in which the minimum installation number and the maximum installation number are equal. Therefore, in the process in which the configuration optimization problem (0, 0, 0) is given, the configuration n ₁ = n ₂ = n ₃ = 0 is output to the running evaluation unit 16 and the initial evaluation unit 17, and the total evaluation unit 18 The evaluation function J is acquired from (step S22). As a matter of course, the configuration n ₁ = n ₂ = n ₃ = 0 does not satisfy the constraint, but in the present embodiment, a penalty is added to the value of the evaluation function J according to the degree of not satisfying the constraint. The evaluation function is stored in the storage unit 12 (step S23), and the configuration n ₁ = n ₂ = n ₃ = 0 is output as the optimal solution 10D of the configuration optimization problem (0, 0, 0). Then, in the process given the configuration optimization problem (0, 0, 1), the solution of the subproblem (0, 0, 0) is calculated (step S 26 is completed), and the process proceeds to the next step .

  Next, the configuration optimization unit 15 checks whether or not there is a solution of the maximum number of configurations (a configuration using the maximum number of installable component devices that can be selected in the problem) for the solution of the subproblem 15A ( Step S27). Specifically, for example, in the process in which the configuration optimization problem (0, 0, 1) is given, the solution of the subproblem (0, 0, 0) has the maximum number of configurations (the components selectable in the problem Check if there is a solution (configuration using the maximum number of installations).

Next, when there is a solution with the maximum number of configurations (YES in step S27), the configuration optimization unit 15 calculates an evaluation function of the maximum number configuration of the given configuration optimization problem (step S28). Then, the configuration optimization unit 15 selects the best solution among the solution of the maximum number configuration of the given configuration optimization problem and the solution of the partial problem (step S29). Here, the solution n ₁ = n ₂ = n ₃ = 0 of the partial problem (0,0,0) of the configuration optimization problem (0,0,1) has the maximum number configuration (step S 26), In step S28, the evaluation function of the maximum number configuration n ₁ = n ₂ = 0 and n ₃ = 1 of the configuration optimization problem (0,0,1) is calculated, and in step S29, the configuration optimization problem (0,0 , 1) The evaluation function to which a penalty is added among the maximum number configuration n ₁ = n ₂ = 0, n ₃ = 1 or the solution n ₁ = n ₂ = n ₃ = 0 of the subproblem (0,0,0) Solve the configuration with the smallest value of.

  On the other hand, when there is no solution with the maximum number configuration (NO in step S27), the best solution among solutions of the partial problem is selected (step S30). That is, the configuration optimization unit 15 calculates the value of the evaluation function to which the penalty is added in the solution of the subproblem 15A when none of the solutions of the subproblems of the subproblem 15A is the maximum number configuration of the problem. Let the smallest configuration be the solution of the given configuration optimization problem 10A.

  Finally, the selected solution and its evaluation function are stored in the storage unit 12 (step S23), and the selected solution is output. Thus, in the recursive process, after the solution of the lower-order (small number of combinations) configuration optimization problem (0, 0, 1) (0, 1, 0) (1, 0, 0) is calculated, Configuration optimization problems (0, 0, 2), (0, 1, 1), (0, 2, 0), (1, 0, 1), (1, 1, 0) , (2, 0, 0) are to be calculated, and the configuration optimization problem 10A is solved in a bottom-up manner. Finally, the solution of the configuration optimization problem (2, 2, 2) in the first call of step S2 is output as the optimal solution (step S3 in FIG. 5).

[Function effect]
In the present embodiment, the solution to a configuration optimization problem 10A is either the maximum number of configurations (configuration using the maximum number of components selectable in the problem) or the solution to the subproblem 15A. . Furthermore, the configuration optimization unit 15 considers that the maximum number configuration may be a solution only if there is a solution with subproblem 15A that is the maximum number configuration of the problem. Reduce the computational load by computing the evaluation function only when there is a possibility of solution.

  According to the present embodiment, since the determination of the configuration of the device and the calculation of the operation optimization can be separated, the calculation model of the operation optimization is simplified as compared with the conventional method of collectively optimizing the configuration and the operation. It is not necessary to do this, and a highly accurate solution can be obtained. In addition, since it is a recursive method, subproblem 15A will be solved in a bottom-up manner, and only the minimum configuration that can be a solution among them is used for operation optimization calculation, so the calculation load is also small. The optimum configuration can be determined within a practical calculation time. Moreover, it is not a random search method but a full search method, and it is considered that a global optimum solution can be obtained except in a specific case.

  Furthermore, in the present embodiment, when there is no solution that satisfies the constraint condition in the subproblem 15A, the configuration optimization unit 15 adds a penalty to the value of the evaluation function according to the degree of not satisfying the constraint condition and then evaluates. The solution of the subproblem 15A satisfies the constraint that the solution of the subproblem 15A has the possibility that the maximum number configuration of the configuration optimization problem 10A may become a solution because the function value is configured to solve the configuration with the minimum value. The calculation load can be further reduced because it can be considered only if the maximum number configuration is used.

[Modification]
Next, modifications of the present embodiment will be described focusing on differences. In this embodiment, when there is no solution that satisfies the constraint condition in the subproblem 15A, a penalty is added to the value of the evaluation function according to the degree of not satisfying the constraint condition, and the configuration in which the value of the evaluation function with the penalty is the smallest Although a solution is assumed, in this modification, the configuration optimization unit 15 does not have a solution when there is no solution that satisfies the constraint condition in the partial problem 15A.

  Furthermore, in this modification, the maximum number configuration of the configuration optimization problem 10A may be a solution not only when the solution of the subproblem 15A is the maximum number configuration of the subproblem, but also the partial It is considered including the case where there is no solution that satisfies the constraint condition for at least one partial problem in the problem 15A (see step S27 in FIG. 6).

  Then, when there is no solution satisfying the constraint condition in the partial problem 15A, or when there is a solution with the maximum number of configurations (YES in step S27 of FIG. 6), the configuration optimization unit 15 gives the optimal configuration. The evaluation function of the maximum number configuration of the optimization problem is calculated (step S28 in FIG. 6). Then, the configuration optimization unit 15 selects the best solution among the solution of the maximum number configuration of the given configuration optimization problem and the solution of the partial problem (step S29 in FIG. 6). That is, the solution of the configuration optimization problem 10A satisfies the constraints among the maximum number configuration of the configuration optimization problem 10A or the solution of the partial problem 15A, and the value of the evaluation function is the smallest.

  On the other hand, if all the solutions of subproblem 15A satisfy the constraints and there is no solution of the maximum number configuration (NO in step S27 of FIG. 6), the best solution among the solutions of the subproblems is selected. (Step S30 in FIG. 6). That is, the configuration optimization unit 15 is configured such that the value of the evaluation function is the smallest among the solutions of the subproblem 15A when none of the solutions of the subproblems of the subproblem 15A is the maximum number configuration of the problem. Is the solution of the configuration optimization problem 10A. Also in the present modification, the same effects as those of the present embodiment can be obtained.

  From the above description, many modifications and other embodiments of the present invention will be apparent to those skilled in the art. Accordingly, the above description should be taken as exemplary only, and is provided for the purpose of teaching those skilled in the art the best mode of carrying out the present invention. The structural and / or functional details may be substantially altered without departing from the spirit of the present invention.

  INDUSTRIAL APPLICABILITY The present invention can be used for facility planning of a distributed energy system configured with a plurality of energy devices such as gas turbines and gas engines.

DESCRIPTION OF SYMBOLS 10 Configuration optimization apparatus 11 Arithmetic unit 12 Storage unit 15 Configuration optimization unit 16 Running evaluation unit 17 Initial evaluation unit 18 Total evaluation unit 20 Distributed energy system 21 Energy supply equipment 30 Consumer 31 Equipment

Claims (2)

A method implemented in a configuration optimization apparatus including an input unit, a storage unit, and a calculation unit, and the configuration of equipment is optimized in the facility planning of a distributed energy system in which energy is supplied by a plurality of energy supply equipment Configuration optimization method, and
Storing in advance in the storage unit information regarding an arbitrary evaluation function and constraint conditions including an operation cost;
Accepting, by the input unit, input of a parameter including information on a configuration optimization problem of the distributed energy system, a demand input condition, and a device database;
By the arithmetic unit, the information on the evaluation function and constraints, as well as to obtain the parameters, obtained the evaluation function and the constraint condition information about the, and, on the basis of the parameter, the evaluation function, the constraint For the configuration optimization problem for which the configuration of the device to be minimized is sought, a configuration optimization problem with a smaller maximum number of selectable components is created as a plurality of partial problems, and the partial problems are solved recursively anda step of solving the original problem by,
In the step of obtaining a solution by the operation unit, when there is no solution satisfying the constraint condition in the subproblem, a penalty is added to the value of the evaluation function according to the degree of not satisfying the constraint condition and a penalty is added. Solving the solution with the smallest value of the evaluation function;
When the solution of at least one of the plurality of partial problems is the maximum number configuration of the partial problems, the solution of the configuration optimization problem is the maximum number configuration of the configuration optimization problems or the plurality Setting the value of the evaluation function to which the penalty is added to a minimum among solutions of the subproblems of
The solution of the configuration optimization problem adds the penalty among the solutions of the plurality of subproblems if the solution of any of the plurality of subproblems is not the maximum number configuration of the subproblems. Setting the value of the evaluation function to a minimum;
How to optimize the configuration of distributed energy systems , including: An optimization apparatus that executes a configuration optimization method for optimizing the configuration of equipment in facility planning of a distributed energy system in which energy is supplied by a plurality of energy supply equipment,
A storage unit for storing in advance information on an arbitrary evaluation function and constraints including an operation cost;
An input unit that receives input of parameters including information related to a configuration optimization problem of the distributed energy system, a demand input condition, and a database of devices;
Information about the evaluation function and constraints, as well as to obtain the parameters, obtained the evaluation function and the constraint condition information about the, and, on the basis of the parameter, the evaluation function, minimization under the constraint For the configuration optimization problem for which the configuration of the target device is to be determined, a configuration optimization problem in which the maximum number of selectable components is one less is created as a plurality of partial problems, and the original problem is solved by solving the partial problem recursively. e Bei a calculation unit, a seeking solutions of,
The calculation unit adds a penalty to the value of the evaluation function according to the degree of failure to satisfy the restriction condition when there is no solution satisfying the restriction condition in the partial problem, and the value of the evaluation function to which the penalty is added is Let us solve the minimum configuration,
When the solution of at least one of the plurality of partial problems is the maximum number configuration of the partial problems, the solution of the configuration optimization problem is the maximum number configuration of the configuration optimization problems or the plurality In the solution of the subproblem of, the value of the evaluation function to which the penalty is added is the smallest configuration,
The solution of the configuration optimization problem adds the penalty among the solutions of the plurality of subproblems if the solution of any of the plurality of subproblems is not the maximum number configuration of the subproblems. An apparatus for optimizing the configuration of a distributed energy system , wherein the value of the evaluation function is minimized .

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